FAIR_Bioinfo analysis
Description of raw data
The objective of this application is to find the differentially expressed genes after using the FAIR_Bioinfo workflow
Conditions
The count data files and associated biological conditions are listed in the following table :
Count table
After loading the data we first have a look at the raw data table itself. The data table contains one row per annotated feature and one column per sequenced sample. Row names of this table are feature IDs (unique identifiers). The table contains raw count values representing the number of reads that map onto the features. For this project, there are 7659 features in the count data table.
Looking at the summary of the count table provides a basic description of these raw counts (min and max values, median, etc).
## CondA_SRR3105699 CondA_SRR3105698 CondA_SRR3105697
## Min. 0.0000 0.0000 0.0000
## 1st Qu. 60.0000 122.0000 68.0000
## Median 147.0000 313.0000 162.0000
## Mean 353.3178 797.3789 402.3951
## 3rd Qu. 364.0000 791.0000 394.0000
## Max. 21362.0000 58521.0000 21909.0000
## CondB_SRR3099587 CondB_SRR3099586 CondB_SRR3099585
## Min. 0.0000 0.0000 0.0000
## 1st Qu. 85.0000 72.0000 40.0000
## Median 208.0000 177.0000 127.0000
## Mean 531.7709 471.8327 466.9897
## 3rd Qu. 505.0000 433.0000 370.5000
## Max. 41471.0000 41359.0000 42448.0000Total read count per sample
Next figure shows the total number of mapped reads for each sample. Reads that map on multiple locations on the transcriptome are counted more than once, as far as they are mapped on less than 50 different loci. We expect total read counts to be similar within conditions, they may be different across conditions. Total counts sometimes vary widely between replicates. This may happen for several reasons, including:
- different rRNA contamination levels between samples (even between biological replicates);
- slight differences between library concentrations, since they may be difficult to measure with high precision.;
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Volcano plot
Parameters
## ColHisto ColCondA ColCondB fitType pvalue
## "#00BFFF" "#FFD700" "#4169E1" "parametric" "0.05"
## logFC pAdjustMethod
## "2" "BH"R session information
The versions of the R software and Bioconductor packages used for this analysis are listed below. It is important to save them if one wants to re-perform the analysis in the same conditions.
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.19.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 shinycssloaders_0.2.0
## [3] shinyjs_1.0 colourpicker_1.0
## [5] shinyWidgets_0.4.4 reshape2_1.4.3
## [7] plotly_4.8.0 ggplot2_3.1.0
## [9] FactoMineR_1.41 DT_0.5
## [11] DESeq2_1.22.2 SummarizedExperiment_1.12.0
## [13] DelayedArray_0.8.0 BiocParallel_1.16.5
## [15] matrixStats_0.54.0 Biobase_2.42.0
## [17] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
## [19] IRanges_2.16.0 S4Vectors_0.20.1
## [21] BiocGenerics_0.28.0 shinydashboard_0.7.1
## [23] shiny_1.2.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_0.9-7 RColorBrewer_1.1-2
## [4] httr_1.4.0 tools_3.5.1 backports_1.1.3
## [7] R6_2.3.0 rpart_4.1-13 Hmisc_4.1-1
## [10] DBI_1.0.0 lazyeval_0.2.1 colorspace_1.3-2
## [13] nnet_7.3-12 withr_2.1.2 tidyselect_0.2.5
## [16] gridExtra_2.3 bit_1.1-14 compiler_3.5.1
## [19] htmlTable_1.13.1 Cairo_1.5-9 flashClust_1.01-2
## [22] scales_1.0.0 checkmate_1.9.0 genefilter_1.64.0
## [25] stringr_1.3.1 digest_0.6.18 foreign_0.8-70
## [28] rmarkdown_1.11 XVector_0.22.0 base64enc_0.1-3
## [31] pkgconfig_2.0.2 htmltools_0.3.6 htmlwidgets_1.3
## [34] rlang_0.3.0.1 rstudioapi_0.8 RSQLite_2.1.1
## [37] bindr_0.1.1 jsonlite_1.6 crosstalk_1.0.0
## [40] acepack_1.4.1 dplyr_0.7.8 RCurl_1.95-4.11
## [43] magrittr_1.5 GenomeInfoDbData_1.2.0 Formula_1.2-3
## [46] leaps_3.0 Matrix_1.2-14 Rcpp_1.0.0
## [49] munsell_0.5.0 yaml_2.2.0 scatterplot3d_0.3-41
## [52] stringi_1.2.4 MASS_7.3-50 zlibbioc_1.28.0
## [55] plyr_1.8.4 grid_3.5.1 blob_1.1.1
## [58] promises_1.0.1 crayon_1.3.4 miniUI_0.1.1.1
## [61] lattice_0.20-35 splines_3.5.1 annotate_1.60.0
## [64] locfit_1.5-9.1 knitr_1.21 pillar_1.3.1
## [67] geneplotter_1.60.0 XML_3.98-1.16 glue_1.3.0
## [70] evaluate_0.12 latticeExtra_0.6-28 data.table_1.11.8
## [73] httpuv_1.4.5 gtable_0.2.0 purrr_0.2.5
## [76] tidyr_0.8.2 assertthat_0.2.0 xfun_0.4
## [79] mime_0.6 xtable_1.8-3 later_0.7.5
## [82] rsconnect_0.8.12 viridisLite_0.3.0 survival_2.42-3
## [85] tibble_1.4.2 AnnotationDbi_1.44.0 memoise_1.1.0
## [88] cluster_2.0.7-1